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Stephen Kissler

@skissler.bsky.social
2.7K followers 1K following 97 posts

Infectious disease epidemiologist / computer science professor @ CU Boulder. Trying to figure out how to keep us all healthy and fed.

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Reposted by Stephen Kissler
Louise H. Moncla @lhmoncla.bsky.social · 28/09/2026
Due to some really great work from the @nextstrain.org team, it is now easier to put together new builds! We've recently released H7Nx and H10Nx builds onto the main avian-flu page along with all the standard features. Check them out! nextstrain.org/avian-flu/h1...
nextstrain.org
auspice
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Stephen Kissler @skissler.bsky.social · 28/09/2026
this is such lovely work!
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Reposted by Stephen Kissler
Sam Abbott @seabbs.bsky.social · 28/09/2026
A few years ago we (Kelly Charniga @sangwoopark.bsky.social et al.) did some thinking on how we should estimate and report epidemiological delay distributions (i.e incubation periods etc. etc.). journals.plos.org/ploscompbiol...
journals.plos.org
Best practices for estimating and reporting epidemiological delay distributions of infectious diseases
Epidemiological delays are key quantities that inform public health policy and clinical practice. They are used as inputs for mathematical and statistical models, which in turn can guide control strat...
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Stephen Kissler @skissler.bsky.social · 10/09/2026
The Global Society for Infectious Disease Dynamics (GSIDD) is hosting a seminar tomorrow (Fri Sep 11) at 1pm Eastern (US) time — these monthly meetings aim to share what's happening in IDD research around the globe and to build connections among researchers and practitioners. More info at the link!
gsidd.org
Events | GSIDD
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Reposted by Stephen Kissler
id-journal.bsky.social @id-journal.bsky.social · 07/09/2026
Dengue-mAb 🦠✅ cut viral load by up to 2.35 log at 24h vs placebo 1.29 (P≤.003). Fever cleared in 2-3.5h vs 26.8h placebo. No serious adverse effects in 250 pts.
jamanetwork.com
Safety and Preliminary Efficacy of Dengue Monoclonal Antibody in Adult Patients: A Randomized Clinical Trial
Importance  Dengue outbreaks are increasing in frequency and intensity. In the absence of any specific treatment, a pan-serotype recombinant monoclonal antibody (Dengue-mAb) was tested in adult patients with dengue as a therapeutic option.Objective  To assess the safety and effect of a single dose of Dengue-mAb on dengue viremia at different dose levels among patients with dengue.Design, Setting, and Participants  A phase 2, single-blind, randomized, placebo-controlled clinical trial was conducted from September 19, 2021, to May 13, 2023, at 12 hospitals in India. Study participants were aged 18 to 60 years with dengue and a history of fever onset within 48 hours. Key exclusion criteria were severe dengue, hemoglobin less than 10 g/dL, absolute neutrophil count less than 500/mm3, total leukocyte count less than 1500/mm3, platelet count less than 50 000/mm3, and any other clinically significant disorders. Data were analyzed from January 11, 2024, to March 26, 2024.Interventions  Participants were randomly allocated 1:1:1:1:1 using an interactive web response system to receive 1 of 4 dose levels of Dengue-mAb (3, 5, 7, or 9 mg/kg) or placebo by slow intravenous infusion.Main Outcomes and Measures  Primary outcomes were reduction in viremia at 24 hours and causally related serious adverse events (SAEs). Secondary outcomes were RNAemia, fever, and hematologic and pharmacokinetic parameters.Findings  A total of 250 participants were randomized, with 50 participants in each group. The overall median age was 30 years (IQR, 24-37 years), and 180 participants (72.3%) were male. There were no causally related SAEs. The adjusted mean (SE) viral log reduction at 24 hours was significantly higher with Dengue-mAb—from 1.93 (0.23) with 3 mg/kg (P = .04) to 2.35 (0.27) with 9 mg/kg (P = .003)—compared with placebo (1.29 [0.22]), using a mixed model for repeated measures. Participants with detectable viremia (n = 32) at baseline were negative for dengue virus by 8 hours when treated with a 5- to 9-mg/kg dose of Dengue-mAb compared with 72 hours with placebo. Median time to fever clearance was 3.5 (95% CI, 1.0-24.0) hours (hazard ratio [HR], 2.6 [95% CI, 1.0-6.4]) with Dengue-mAb 5 mg/kg (n = 14) and 2.0 (95% CI, 0.5-6.0) hours (HR, 2.8 [95% CI, 1.2-6.8]) with Dengue-mAb 7 mg/kg (n = 14), compared with 26.8 (95% CI, 1.0-50.5) hours with placebo (n = 12). At 24 hours, between 13 of 14 participants (92.9%; 95% CI, 66.1%-99.8%) with Dengue-mAb (3 mg/kg), 14 of 14 participants (100.0%; 95% CI, 76.8%-100.0%) with Dengue-mAb (5 mg/kg and 7 mg/kg), and 16 of 17 participants (94.1%; 95% CI 71.3%-99.9%) with Dengue-mAb (9 mg/kg); and 7 of 12 participants (58.3%; 95% CI, 27.7%-84.8%) with placebo had fever clearance.Conclusions and Relevance  In this randomized clinical trial, Dengue-mAb was safe and well tolerated. Dengue-mAb rapidly reduced dengue viremia and fever at doses of 5 mg/kg and 7 mg/kg, making this the first therapeutic against wild-type dengue virus to show preliminary efficacy in humans.Trial Registration  Clinical Trial Registry-India Identifier: CTRI/2021/07/035290
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Reposted by Stephen Kissler
Dr Emma Hodcroft @firefoxx66.bsky.social · 27/08/2026
I'm so incredibly proud of what we've built in these past two years! But I also can't wait to see what comes next - we've still got so much more do to! Happy Birthday Pathoplexus! 🎂
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Reposted by Stephen Kissler
Nature Portfolio @natureportfolio.nature.com · 24/08/2026
County vaccination figures looked reassuring until the largest US measles outbreak in 30 years began. Ana Bento tells the story of building a 50,000-school dataset, published in Nature Medicine, to find out why the averages were lying, and what surveillance looks like once you finally zoom in. 🧪
go.nature.com
The hidden map of measles risk: why zooming in changed everything
County vaccination figures looked reassuring right up until the largest US measles outbreak in 30 years began. This is the story of building a 50,000-school dataset to find out why the averages were lying, and what surveillance looks like once you finally zoom in.
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Reposted by Stephen Kissler
Gabriel Bosslet @gbosslet.bsky.social · 03/08/2026
We opened an ICU that had been shuttered for well over a decade so that a hospital that has 18 MICU beds could fit 46 patients on ventilators
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Stephen Kissler @skissler.bsky.social · 30/07/2026
Thanks for having a look!
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Stephen Kissler @skissler.bsky.social · 29/07/2026
Thank you! Yes, definitely — I think this left more interesting questions than it resolved! Our main goals in the immediate future are checking how robust the "burstiness" signal is across pathogens, variants, data streams, etc; and digging deeper into the implications for intervention design.
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Stephen Kissler @skissler.bsky.social · 29/07/2026
yes (I had this experience!) --- there could be other explanations too (partner's immune system building up mucosal immunity due to multiple low-level exposures, idiosyncrasies of the room's ventilation...) but I think burstiness could at least be *part* of the story
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Stephen Kissler @skissler.bsky.social · 29/07/2026
This was, in the end, a joyful romp through some fascinating corners of epidemiological theory. I hope you find it interesting and useful! As always, I welcome and appreciate feedback!
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Stephen Kissler @skissler.bsky.social · 29/07/2026
Still, we think there's good reason to believe burstiness is a genuine phenomenon, and that it completes a conceptual quartet with R0 (the reproduction number), k (overdispersion), and the GI distribution. Lots of exciting questions about estimation, evolution, and epidemiological imapcts remain!
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Stephen Kissler @skissler.bsky.social · 29/07/2026
There are lots of important caveats: we intentionally only considered simple models of infectiousness timing (no rebounds!); estimating burstiness from serial intervals is subject to many biases; and we couldn't cleanly determine how much burstiness is driven by biology vs. contact patterns.
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Stephen Kissler @skissler.bsky.social · 29/07/2026
Finally, bursty infectiousness makes it harder to estimate the epidemic growth rate and the GI distribution itself: daily case counts become more variable, and time-clustering among secondary infections reduces the effective amount of information available about the GI distribution's shape.
Figure with three panels depicting how bursty infectiousness makes daily case counts more variable, using simulations parameterized for Measles.
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Stephen Kissler @skissler.bsky.social · 29/07/2026
Bursty infectiousness can make detect-and-isolate interventions extremely sensitive to detection timing: small delays can cause effectiveness to plummet from >95% to <5%. Burstiness also modulates how these interventions impact overdispersion (superspreading) and the epidemic growth rate.
Figure with two panels depicting how burstiness impacts testing effectiveness under symptom-based and test-based detection. When infectiousness is bursty (darker lines), the interventions are far more sensitive to small shifts in detection time.
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Stephen Kissler @skissler.bsky.social · 29/07/2026
Bursty infectiousness also makes epidemic timing more variable: for example, an early-epidemic burst that happens late (in the tail of the GI distribution) can delay the entire epidemic's trajectory. This builds on some fascinating theory developed by Dylan Morris, John Maclean, & Andrew Black:
link.springer.com
Computation of random time-shift distributions for stochastic population models - Journal of Mathematical Biology
Even in large systems, the effect of noise arising from when populations are initially small can persist to be measurable on the macroscale. A deterministic approximation to a stochastic model will fa...
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Stephen Kissler @skissler.bsky.social · 29/07/2026
How about epidemiological consequences? There are many: when contacts vary over time, bursty infectiousness can capitalize on brief, high-contact periods, creating "coincidence superspreading", which requires neither the "super-contactors" or "super-shedders" typically implicated in superspreading.
Figure with three panels depicting how the interaction between bursty infectiousness and time-varying contact patterns creates interpersonal variation (overdispersion) in the amount of infectiousness
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Stephen Kissler @skissler.bsky.social · 29/07/2026
Viral kinetics data from SARS-CoV-2 offer further evidence: the span of time individuals typically spend over viral load thresholds consistent with appreciable infectiousness is on the order of ~1 day, possibly up to 3–4 – but much shorter than the oft-quoted infectiousness period.
Figure with two panels depicting estimates of the burstiness parameter ψ for ten pathogens, along with the time spent above viral load thresholds consistent with infectiousness for SARS-CoV-2.
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Stephen Kissler @skissler.bsky.social · 29/07/2026
Serial intervals from the amazing OutbreakTrees database (@julianataube.bsky.social & colleagues) suggest burstiness exists: for measles, influenza, and SARS-CoV-2, we found good evidence of burstiness (ψ near zero), while for norovirus, infectiousness appears more sustained.
journals.plos.org
An open-access database of infectious disease transmission trees to explore superspreader epidemiology
This study compiles and standardizes reported infectious disease transmission trees to analyze trends in superspreader frequency and generation; these data support theories that intermediate dispersio...
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Stephen Kissler @skissler.bsky.social · 29/07/2026
Conceptually, ψ is a sibling of the overdispersion parameter k: it captures individual-level variation around a population mean (the GI distribution for ψ; R0 for k). We know that k varies across pathogens and has all sorts of impacts (superspreading!). What about ψ?
nature.com
Superspreading and the effect of individual variation on disease emergence - Nature
From Typhoid Mary to SARS, it has long been known that some people spread disease more than others. But for diseases transmitted via casual contact, contagiousness arises from a plethora of social and...
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Stephen Kissler @skissler.bsky.social · 29/07/2026
When infectiousness is bursty (ψ=0), infectiousness happens at a single moment—but that moment is distributed across individuals according to the GI distribution. When infectiousness is sustained (ψ=1), the individual and overall GI distributions are identical; a person may transmit over many days.
Figure with four panels depicting how the "burstiness" parameter ψ shapes the individual generation interval distribution relative to the overall, population-average generation interval distribution.
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Stephen Kissler @skissler.bsky.social · 29/07/2026
This is our first contribution: we present three tractable "burst" models. You begin by defining the GI distribution (as a Gamma or Log-normal) – then, with a parameter ψ, you can tune the width of the individual GI distribution, from maximally bursty (ψ=0) to maximally sustained (ψ=1).
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Stephen Kissler @skissler.bsky.social · 29/07/2026
To test this, and to assess its epidemiological impact, we need a model that can tune the width of the "individual GI distribution" while keeping the overall GI distribution fixed. Otherwise, changing the span of individual infectiousness also changes the population average, conflating two effects.
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Stephen Kissler @skissler.bsky.social · 29/07/2026
Why the discrepancy? The longer spans reflect the generation interval (GI) distribution — the time from one infection to the next, as a population average. But individual-level infectiousness need not resemble the GI distribution: infectiousness could plausibly be concentrated in a short "burst".
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Stephen Kissler @skissler.bsky.social · 29/07/2026
This paper by Ashish Goyal & colleagues was a big inspiration: using multiple data sources, they found that appreciable influenza and SARS-CoV-2 infectiousness may last less than a day — though their infectiousness windows are often quoted as 6-8 and 9-12 days!
elifesciences.org
Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events
A mathematical model was used to establish a simple conceptual basis for why super-spreader events fundamentally drive the spread of SARS-CoV-2 but not influenza.
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Stephen Kissler @skissler.bsky.social · 29/07/2026
I'm excited to share a preprint that's been years in the making: the idea took shape during the COVID pandemic. Anecdotally, people would sometimes spread COVID at work but not at home, and vice versa. Why? One explanation would be if infectiousness is fleeting...
medrxiv.org
How bursty infectiousness shapes epidemic dynamics
An epidemic’s expected course is determined by the magnitude and timing of a typical person’s infectiousness — captured, in turn, by the basic reproduction number and the generation-time distribution....
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Reposted by Stephen Kissler
The Ferrari Lab @theferrarilab.bsky.social · 28/07/2026
In 2017, while working at Epicentre/Medecins Sans Frontieres I helped to develop the Urgepi program in southeast DRC. What started as measles surveillance program has matured into a prioritization scheme for outbreak prevention and response that serves 15m people (land area ~Spain).
medrxiv.org
Risk-targeted interventions for measles control- Lessons from an emergency response project in the Katanga Region, Democratic Republic of the Congo (2021-2023)
The Urgepi project is a measles emergency initiative implemented by Medecins Sans Frontieres Operational Centre Paris (MSF-OCP) in the Katanga region of the Democratic Republic of Congo, a setting cha...
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Stephen Kissler @skissler.bsky.social · 28/07/2026
I was giving my introductory talk for a new fellowship I was very excited about, right after finishing my PhD in applied math. First question: “so, do you actually advance mathematics, or are you just a consumer of math?”
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Stephen Kissler @skissler.bsky.social · 28/07/2026
Another preprint led by @hadley-liza.bsky.social (who is on a roll!) now out on medRxiv: how can we infer measles vaccination rates given incomplete data? This one's the result of an incredible collaboration with @papsti.bsky.social & others at the Public Health Agency of Canada.
medrxiv.org
Making inferences with incomplete epidemiological data: a proof-of-concept estimating measles vaccine coverage across Canada
Background & aims of study Current measles outbreaks around the world have highlighted the evolving immunity landscape of some vaccine-preventable diseases (VPDs). In Canada, there have been approxima...
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Reposted by Stephen Kissler
Carl T. Bergstrom @carlbergstrom.com · 23/07/2026
1. The new Trump / Kratsios plan for US science appears explicitly designed to destroy US universities. Diverting money to tech donors is probably just a bonus. Gift link.
nytimes.com
Trump’s Plan for Science: More Money for A.I., Less for Universities (Gift Article)
Michael Kratsios, President Trump’s science adviser, proposed overhauling how the government funds research. Democrats said Mr. Trump’s actions had weakened science.
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Stephen Kissler @skissler.bsky.social · 23/07/2026
Was an honor to get to play a small role in this — congrats @hadley-liza.bsky.social on a really nice article on strengthening communication between scientists and policymakers! www.nature.com/articles/s44...
nature.com
How understanding the diversity of perspectives and systems in governments can increase the impact of scientific research - Communications Health
Communications Health - How understanding the diversity of perspectives and systems in governments can increase the impact of scientific research
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Reposted by Stephen Kissler
Erin Biba @erinbiba.bsky.social · 20/07/2026
This is a *fantastic* video that explains exactly WTF is going on with Taylor Farms and how tracing works TLDR: - they are responsible - the false positive was only on one of several samples - it’s difficult to test parasites - there may still be other sources - Taylor Farms is being shady AF
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Reposted by Stephen Kissler
Bill Hanage @billhanage.bsky.social · 17/07/2026
👇🏼💯
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Reposted by Stephen Kissler
STAT @statnews.com · 15/07/2026
An analysis of public comments on a White House proposal to change the way federal contracts and grants are doled out shows widespread opposition. www.statnews.com/2026/07/15/t...
statnews.com
Flood of comments on White House grantmaking overhaul is largely negative, analysis shows
An analysis of public comments on a White House proposal to change the way federal contracts and grants are doled out shows widespread opposition
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Reposted by Stephen Kissler
APHA @apha.org · 13/07/2026
Today is the last day to tell OMB not to put politics over science and public health.
A title slide shows a photo of an entrance to the National Institutes of Health by hoto by G. Edward Johnson with a black gradient over it along with the words, "Why is scientific research under threat?" At the bottom is the APHA logoWhite text in a navy blue box says, "In early 2025, the Trump administration abruptly canceled NIH grants for research that did not align with its policies. Studies on vaccine hesitancy, LGBTQIA+ health, Black maternal mortality and more were defunded, leaving many researchers unemployed." Beneath the box is a photo by Geoff Livingston of a protest around the Lincoln Memorial Reflecting Pool where people are holding signs. The sign in front says, "Explain how gutting the NIH makes America great."White text on a blue background says, "APHA and other organizations fought back, suing the NIH. Now, the administration is looking to circumvent the courts by fundamentally changing the grant process through the Office of Management and Budget." Beneath that is black text over an image of a ripped piece of paper that says, “In spring 2025, the American Civil Liberties Union joined the American Public Health Association and a union representing many postdocs in suing the NIH, arguing the terminations were arbitrary and capricious — issued, as Sosa put it, without legal basis. Courts agreed in early rounds, and grants began to be restored.” Medscape, June 29, 2026. That is a quote from this article: https://www.medscape.com/viewarticle/science-interrupted-lasting-impact-terminated-grants-2026a1000m1l?form=fpfWhite and yellow text on a black background says, "OMB’s proposal would: Authorize political appointees to award federal grants based on political ideology instead of scientists and policy experts awarding grants based on evidence-based research; Allow those appointees to cancel, suspend or modify grants at any time and for any reason; Block federal funding for implementing programs that include DEIA, “gender ideology,” and international collaboration; Restrict the use of grant funds for professional development like attending conferences"
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Stephen Kissler @skissler.bsky.social · 13/07/2026
Done.
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Reposted by Stephen Kissler
Casey Middleton, PhD @caseymiddleton.bsky.social · 09/07/2026
Throughout your scientific career, you will read hundreds of dense manuscripts. 📚 As the literature stack piles up, remembering exactly where you saw a specific methodology or finding can be tough. Here is how I use a relational database to keep track of it all (and et al.). 👇
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Max Kozlov @maxkozlov.bsky.social · 27/06/2026
NIH grants have been under political scrutiny since early 2025. But what does the screening process actually look like? What is being flagged? I spent a month digging into leaked docs and emails. We've published the 235-word list that are being flagged by an internal AI tool. And so much more. 🧵
nature.com
Inside the new political screening that’s stalling NIH grants
Mandatory reviews by top health officials and checks for 235 disfavoured terms have left hundreds of vetted grant applications in administrative limbo.
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Reposted by Stephen Kissler
Climate.us @climate.us · 26/06/2026
We got a big bump in traffic yesterday when people learned about us on NPR. Thank you to @npr.org for covering our launch and bringing our story to people's attention! And thank you to all our new followers. We can do this together! 🌍️🧪💪 #ClimateAction #ClimateForUs #ProtectScience
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Stephen Kissler @skissler.bsky.social · 19/06/2026
I know it’s like 99.999% surely just run of the mill flu, but I don’t love the parallel with the start of the 1918 pandemic en.wikipedia.org/wiki/Spanish...
en.wikipedia.org
Spanish flu - Wikipedia
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Reposted by Stephen Kissler
Pamela Martinez @pamelapm.bsky.social · 29/05/2026
I'm very excited to share this preprint led by @sorenlouislarsen.bsky.social and Chloe Yand, and made possible by the collaboration with my friend and colleague @ayesha-mahmud.bsky.social. www.medrxiv.org/content/10.6...
medrxiv.org
Same household, different choices: variation in health behaviors related to respiratory viruses in Illinois
While SARS-CoV-2 and influenza continue to place a significant burden on population health, within-household differences in decisions towards vaccination and seeking care across these two pathogens, a...
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Reposted by Stephen Kissler
Madhu Pai, MD, PhD @madhupai.bsky.social · 22/05/2026
A year before he passed away, I interviewed Dr Paul Farmer @pih.org about his book on Ebola So relevant right now, especially the dangers of “control-over-care approach” - i.e. isolation & quarantine, but fails to ensure adequate clinical care to the sick www.forbes.com/sites/madhuk...
forbes.com
Ebola, Covid-19 And The Elusive Quest For Global Health Equity
Published during this pandemic, Paul Farmer’s new book, “Fevers, Feuds, And Diamonds,” is important, as it presents a critical dissection of the Ebola outbreak in West Africa, and the lessons we can l...
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Stephen Kissler @skissler.bsky.social · 22/05/2026
Thank goodness, now there's somewhere to submit that project I've been stalled out on for three years...
Screenshot of an email inbox with a message from the journal PLOS titled "submit your aging research."
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Reposted by Stephen Kissler
Casey Middleton, PhD @caseymiddleton.bsky.social · 19/05/2026
🚨 Disease modelers: Do you use empirical VE estimates interchangeably to inform vaccine effect parameters? Me too, and it’s distorting our predictions. VE estimates often do not align with model parameters. We discuss these pitfalls & solutions in our new preprint: arxiv.org/abs/2605.18571 🧵👇
arxiv.org
Incorporating vaccine effects into epidemiological models: common pitfalls and solutions
Incorporating vaccination into mathematical models appears deceptively simple: models integrate vaccine-derived protections, such as reduced susceptibility to infection, using parameters informed by e...
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Reposted by Stephen Kissler
Michael Plank @michaelplanknz.bsky.social · 20/05/2026
The true size of the DRC Ebola outbreak is uncertain. Imperial modelling team estimates that as of 17 May 400-800 cases had occurred, but can't rule out more than 1000. Great work by Anne Cori and team www.imperial.ac.uk/mrc-global-i...
imperial.ac.uk
Report - Estimation of the size of the outbreak of Ebola disease caused by Bundibugyo virus in the Democratic Republic of the Congo
Report - Estimation of the size of the outbreak of Ebola disease caused by Bundibugyo virus in the Democratic Republic of the Congo
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Helen Branswell 🇨🇦 @helenbranswell.bsky.social · 17/05/2026
1. #WHO has declared the #Ebola #Bundibugyo outbreak in NE DRC a public health emergency of international concern, aka a PHEIC. Yet another sign this is likely to be a very challenging outbreak. So far there've been 8 confirmed cases, 246 suspected cases & 80 suspected deaths.
who.int
Epidemic of Ebola Disease caused by Bundibugyo virus in the Democratic Republic of the Congo and Uganda determined a public health emergency of international concern
Pursuant to paragraph 2 of Article 12 - Determination of a public health emergency of international concern, including a pandemic emergency of the International Health Regulations (2005) (IHR), the Di...
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Stephen Kissler @skissler.bsky.social · 15/05/2026
It was an absolute joy to collaborate on this with some real-deal visualization specialists from the Alan Turing Institute: Nick Holliman, Kai Xu, Edyta Bogucka, Xinhuan Shu, and Daniel Archambault; along with closer-to-home colleagues Kath Landgren and Ellen DeGennaro. Feedback is very welcome!
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Stephen Kissler @skissler.bsky.social · 15/05/2026
We hope this offers not so much a roadmap as a contribution to a larger conversation about how we can best create visualizations that are meant not just for other scientists, but for stakeholders in the broadest sense.
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Stephen Kissler @skissler.bsky.social · 15/05/2026
In the manuscript, we break down some fundamentals of model viz that are especially relevant for epidemiological models, including uncertainty visualization, time-course visualization, and interactivity. And, we outline ways of measuring whether a model viz has done a good job.
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